1. Field of the Invention
This invention is in the field of systems and methods for automated vehicle problem diagnosis and maintenance.
2. Description of the Related Art
Modern vehicles, such as automobiles, typically have a variety of built in sensors, as well as various microcontrollers, microprocessors (processors) designed to detect a variety of different problems. These sensors can detect problems such as improper fuel/air mixes, alternator problems, overheating, low oil or water levels, improper tire pressure, low brake fluid, and other types of component malfunctions.
Nonetheless, there remain many situations where such built-in sensors fail to detect various types of worn or damaged vehicle components. To compensate for this problem, automobile users, for example, are often instructed to bring their vehicles in for maintenance inspections at various standard intervals (e.g. every 5,000 miles). During these maintenance inspections, a human mechanic will visually inspect various portions of the vehicle, such as the engine and suspension. The mechanic will, for example, visually note various engine problems such as cracked or broken radiator hoses, worn belts (e.g. fan belts, alternator belts), as well as note various suspension problems such as broken or slipped leaf springs, leaking steering racks, and the like.
These prior art inspections are thus done manually. They tended to rely heavily upon the ability of the mechanic to look at the exposed surfaces of various vehicle components, and detect problems by eye.
Although the most vehicle inspections are done manually, a few automated methods of inspecting vehicle components, or vehicle related components, are also known in the art. For example, McAlfee et. al., in US patent publication 2012/0290259, disclosed a portable optical metrology inspection station and method of operation. In this disclosure, parts (which could be isolated vehicle components) were placed in a cabinet, and a multi-axis robotic arm moved the part while the part was scanned by an optical metrology scanner. Computer software then analyzed the geometric dimensions of the isolated part versus a 3D CAD model.
By contrast, Yang et. al., in U.S. Pat. No. 5,506,683 taught a non-contact measuring apparatus and method for the section profile of a tire, which is another type of isolated vehicle component. In this approach, the 3D profile of automobile tires was determined by using a robotic hand to move a slit-ray generator and camera in multiple axes over the surface of a tire, thus determining the tire section profile shape.
Other prior art, such as Chinese patent CN 203125521U teaches 3D scanning for automobile assembly line inspection. Easterly, U.S. Pat. No. 8,443,301 teaches reporting on vehicle problems using a three dimensional visual interface, but fails to teach automated inspection methods.
In different but related art, Nagle, et. al., in U.S. Pat. No. 8,405,837, disclosed a system and method for inspecting rail road track surfaces using optical wavelength filtering. He taught projecting a laser beam at the rail road track surface, receiving reflected light using a camera, and using a processor to analyze the railroad track bed for deviations in proper crosstie placement.
Modern 3D scanning technology now enables automated sensors to acquire a large amount of sophisticated information regarding the shape and status of various 3D objects. Such sensors include, for example, time-of-flight cameras. Time of flight 3D scanning cameras and methods are described in detail in Hansard, Lee, Choi, and Horaud, “Time-of-Flight Cameras: Principles, Methods, and Applications” (2012). Springer, ISBN 978-1-4471-4657-5. Other types of non-contact 3D scanning methods include triangulation type 3D scanners, structured light scanners, stereoscopic 3D scanners, and the like.
Additionally, modern computer vision automated analysis methods, using modern computer processors and algorithms, has now developed to the point where automated image recognition and inspection methods are now capable of relatively sophisticated analysis. Such methods are discussed in detail in Prince, “Computer Vision: Models, Learning, and Inference” (2012), Cambridge University Press, ISBN 978-1107011793. Other discussion can also be found in Hartley, “Multiple View Geometry in Computer Vision” (2004), Cambridge University Press, ISBN 978-0521540513; as well as Bishop, “Pattern Recognition and Machine Learning” (2007), Springer, ISBN 978-0387310732; Szeliski, “Computer Vision: Algorithms and Applications (Texts in Computer Science) (2010), ISBN 978-1848829343, and the like.
Additionally, modern robotic systems are now capable of very precise automated control and positioning. Such robotic positioning methods are discussed in Jazar, “Theory of Applied Robotics: Kinematics, Dynamics, and Control (2nd Edition)” (2010), Springer ISBN 978-1441917492, and elsewhere.